Parkinson’s Disease Detection Through Visual Deep Learning

Awatramani, Vasudev; Gupta, Deepak

doi:10.1007/978-981-15-5148-2_83

Cited by 4 publications

(2 citation statements)

References 15 publications

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“…The HandPd dataset, which contains images of handwritten spirals and a Meander template of PD and healthy individuals, was used by [ 28 ] in order to detect Parkinson’s disease in its early stages. The images were applied to a transfer learning–deep learning process which aimed to detect the disease.…”

Section: Related Workmentioning

confidence: 99%

Modified SqueezeNet Architecture for Parkinson’s Disease Detection Based on Keypress Data

et al. 2022

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Parkinson’s disease (PD) is the most common form of Parkinsonism, which is a group of neurological disorders with PD-like motor impairments. The disease affects over 6 million people worldwide and is characterized by motor and non-motor symptoms. The affected person has trouble in controlling movements, which may affect simple daily-life tasks, such as typing on a computer. We propose the application of a modified SqueezeNet convolutional neural network (CNN) for detecting PD based on the subject’s key-typing patterns. First, the data are pre-processed using data standardization and the Synthetic Minority Oversampling Technique (SMOTE), and then a Continuous Wavelet Transformation is applied to generate spectrograms used for training and testing a modified SqueezeNet model. The modified SqueezeNet model achieved an accuracy of 90%, representing a noticeable improvement in comparison to other approaches.

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Section: Related Workmentioning

confidence: 99%

Modified SqueezeNet Architecture for Parkinson’s Disease Detection Based on Keypress Data

et al. 2022

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“…In the work of Awatramani and Gupta (2020), the handwritten examinations from the HandPd data set, which contains images of spirals and Meander template, were used. The images were submitted to a transfer learning deep learning process aiming to detect the disease in its early stage.…”

Section: Related Workmentioning

confidence: 99%

A hybrid two-stage SqueezeNet and support vector machine system for Parkinson’s disease detection based on handwritten spiral patterns

Bernardo,

Damaševičius,

de Albuquerque

et al. 2021

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Parkinson's disease (PD) is the second most common neurological disorder in the world. Nowadays, it is estimated that it affects from 2% to 3% of the global population over 65 years old. In clinical environments, a spiral drawing task is performed to help to obtain the disease's diagnosis. The spiral trajectory differs between people with PD and healthy ones. This paper aims to analyze differences between handmade drawings of PD patients and healthy subjects by applying the SqueezeNet convolutional neural network (CNN) model as a feature extractor, and a support vector machine (SVM) as a classifier. The dataset used for training and testing consists of 514 handwritten draws of Archimedes' spiral images derived from heterogeneous sources (digital and paper-based), from which 296 correspond to PD patients and 218 to healthy subjects. To extract features using the proposed CNN, a model is trained and 20% of its data is used for testing. Feature extraction results in 512 features, which are used for SVM training and testing, while the performance is compared with that of other machine learning classifiers such as a Gaussian naive Bayes (GNB) classifier (82.61%) and a random forest (RF) (87.38%). The proposed method displays an accuracy of 91.26%, which represents an improvement when compared to pure CNN-based models such as SqueezeNet (85.29%), VGG11 (87.25%), and ResNet (89.22%).

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Early Detection of Covid-19 Based on Preliminary Features Using Machine Learning Algorithms

Sharma

Prakash

Kumari

et al. 2021

Advances in Intelligent Systems and Computing

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Parkinson’s Disease Detection Through Visual Deep Learning

Cited by 4 publications

References 15 publications

Modified SqueezeNet Architecture for Parkinson’s Disease Detection Based on Keypress Data

Modified SqueezeNet Architecture for Parkinson’s Disease Detection Based on Keypress Data

A hybrid two-stage SqueezeNet and support vector machine system for Parkinson’s disease detection based on handwritten spiral patterns

Early Detection of Covid-19 Based on Preliminary Features Using Machine Learning Algorithms

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